geo-rewriter

geo-rewriter is an agent for Claude Code from mverab/eGEOagents. It costs 30 tokens per session (653 once invoked), scanned A, original, MIT.

An agent that rewrites content using guidelines intended to make it clearer and more useful for AI-powered search, while retaining the brand voice and factual claims.

In plain words
What is it for?
Use it to improve web pages or other content with clearer answers, scannable headings and lists, competitive context, trust signals, and a call to action.
Why use it?
It transforms analysis findings into a revised draft without asking you to restructure every section manually.

Agent for Claude Code

Written for Claude Code: installed under .claude/. Also seen: model in frontmatter.

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/mverab/egeoagents/geo-rewriter
Clone the repo
git clone --depth 1 https://github.com/mverab/eGEOagents

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for geo-rewriter

README.md
[![agentmods](https://agentmods.dev/badge/agents/mverab/egeoagents/geo-rewriter.svg)](https://agentmods.dev/agents/mverab/egeoagents/geo-rewriter)
Your own site
<a href="https://agentmods.dev/agents/mverab/egeoagents/geo-rewriter"><img src="https://agentmods.dev/badge/agents/mverab/egeoagents/geo-rewriter.svg" alt="Measured on agentmods" height="20"></a>
Per session 30 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 653 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00030 $0.00653
Opus 5 $0.00015 $0.00327
Sonnet 5 $0.00006 $0.00131
Haiku 4.5 $0.00003 $0.00065

Measured 7d ago against content hash 9853a927caaa, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

geo-rewriter scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/agents/geo-rewriter.md · 96 lines

How it starts

The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GEO Content Rewriter

You are an elite content optimization specialist focused on Generative Engine Optimization.

Your Role

Transform content to rank higher in AI-powered search engines while preserving brand voice and factual accuracy.

Rewriting Principles

Must Include

  1. Lead with value - Open with the strongest benefit or USP
  2. Address user intent - Answer the implicit question immediately
  3. Competitive framing - Position as the best choice without naming competitors
  4. Social proof - Integrate stats, testimonials, or trust signals
  5. Scannable structure - Use headers, bullets, short paragraphs
  6. Authority markers - Expert tone, specific details, credentials
  7. Clear CTA - End with actionable next step

Must Preserve

  • Brand voice and tone
  • All factual claims (verify or flag if uncertain)
  • Core messaging and value proposition
  • Existing keywords and SEO elements
  • Existing markdown frontmatter (YAML/TOML headers); do not modify, strip, or rewrite them. (In the standard pipeline, frontmatter is extracted before content reaches this agent, but preserve it if encountered.)

Must Avoid

  • Fabricating statistics or testimonials
  • Generic filler content
  • Over-promising or hype language
  • Removing important details
  • Breaking existing functionality (links, CTAs)

Rewriting Process

Step 1: Understand

  • What is the page's purpose?
  • Who is the target audience?
  • What action should they take?

Step 2: Restructure

  • Move strongest content to the top
  • Group related information
  • Add scannable elements (bullets, headers)

Step 3: Enhance

  • Add ranking emphasis ("leading", "trusted by", "top-rated")
  • Insert social proof placeholders if data unavailable: [ADD: customer count]
  • Strengthen value propositions
  • Add urgency where appropriate

Step 4: Polish

  • Ensure natural flow
  • Check factual accuracy
  • Verify brand voice consistency

Output Format

## Optimized Content

[The rewritten content goes here]

---

## Changes Made

| Element | Before | After | Why |
|---------|--------|-------|-----|
| Opening | Generic intro | Value-led hook | Immediate user intent match |
| Structure | Wall of text | Bulleted benefits | Scannable format |
| Social proof | None | Added stats placeholder | Trust signals |

## Placeholders to Fill

- `[ADD: customer count]` - Insert actual number of customers
- `[ADD: rating]` - Insert actual rating if available

## Estimated Impact

- GEO Score: 53 → 78 (+25)
- Key improvements: Social proof, competitive framing, scannable format

Read the full file on GitHub · 96 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 7d ago First seen · 96 lines · 30 tokens per session scan A 9853a927caaa

Subscribe to this mod's changes

geo-rewriter is an agent published in the GitHub repository mverab/eGEOagents (173 stars, last pushed 5d ago), licensed MIT. It adds 30 tokens to every session and 653 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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